{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "given-exhaust",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.386763Z",
     "iopub.status.busy": "2021-03-18T08:36:38.385515Z",
     "iopub.status.idle": "2021-03-18T08:36:38.388025Z",
     "shell.execute_reply": "2021-03-18T08:36:38.389004Z"
    },
    "papermill": {
     "duration": 0.056805,
     "end_time": "2021-03-18T08:36:38.389457",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.332652",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR10\" # CIFAR10, CIFAR100, SVHN\n",
    "v_type = \"mingd\" # mingd, rand\n",
    "root_path = \"\"\n",
    "params_path = \"\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "instant-front",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.462300Z",
     "iopub.status.busy": "2021-03-18T08:36:38.461632Z",
     "iopub.status.idle": "2021-03-18T08:36:38.464100Z",
     "shell.execute_reply": "2021-03-18T08:36:38.464623Z"
    },
    "papermill": {
     "duration": 0.038225,
     "end_time": "2021-03-18T08:36:38.464803",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.426578",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR10\"\n",
    "v_type = \"mingd\"\n",
    "root_path = \"/home/mohammad/transferability/files\"\n",
    "params_path = \"/home/mohammad/transferability/src\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "solar-chess",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.542117Z",
     "iopub.status.busy": "2021-03-18T08:36:38.541171Z",
     "iopub.status.idle": "2021-03-18T08:36:38.545234Z",
     "shell.execute_reply": "2021-03-18T08:36:38.545960Z"
    },
    "papermill": {
     "duration": 0.045744,
     "end_time": "2021-03-18T08:36:38.546187",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.500443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# CIFAR10 - MinGD results"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown\n",
    "v_type2disp = dict(mingd=\"MinGD\", rand=\"Blind Walk\")\n",
    "display(Markdown(f\"# {dataset} - {v_type2disp[v_type]} results\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "inner-settle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.635808Z",
     "iopub.status.busy": "2021-03-18T08:36:38.635254Z",
     "iopub.status.idle": "2021-03-18T08:36:39.212053Z",
     "shell.execute_reply": "2021-03-18T08:36:39.211138Z"
    },
    "papermill": {
     "duration": 0.624849,
     "end_time": "2021-03-18T08:36:39.212289",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.587440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.insert(0, params_path)\n",
    "\n",
    "import argparse, params, time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from importlib import reload\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import random\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "naked-joining",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:39.284695Z",
     "iopub.status.busy": "2021-03-18T08:36:39.284201Z",
     "iopub.status.idle": "2021-03-18T08:36:39.904866Z",
     "shell.execute_reply": "2021-03-18T08:36:39.905843Z"
    },
    "papermill": {
     "duration": 0.656502,
     "end_time": "2021-03-18T08:36:39.906143",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.249641",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "vulnerable-niger",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:39.979160Z",
     "iopub.status.busy": "2021-03-18T08:36:39.978492Z",
     "iopub.status.idle": "2021-03-18T08:36:39.981750Z",
     "shell.execute_reply": "2021-03-18T08:36:39.981099Z"
    },
    "papermill": {
     "duration": 0.039016,
     "end_time": "2021-03-18T08:36:39.981897",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.942881",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import combine_pvalues, ttest_ind_from_stats, ttest_ind\n",
    "from functools import reduce\n",
    "from scipy.stats import hmean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-threshold",
   "metadata": {
    "papermill": {
     "duration": 0.029219,
     "end_time": "2021-03-18T08:36:40.046437",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.017218",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Use the full vectors to train a linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "located-wages",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.112660Z",
     "iopub.status.busy": "2021-03-18T08:36:40.112020Z",
     "iopub.status.idle": "2021-03-18T08:36:40.115518Z",
     "shell.execute_reply": "2021-03-18T08:36:40.115026Z"
    },
    "papermill": {
     "duration": 0.039316,
     "end_time": "2021-03-18T08:36:40.115650",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.076334",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root = os.path.join(root_path,dataset)\n",
    "model_types = ['zero-shot', 'prune', 'fine-tune', 'extract-label', 'extract-logit', 'distillation', 'teacher']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tamil-monte",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.191099Z",
     "iopub.status.busy": "2021-03-18T08:36:40.190676Z",
     "iopub.status.idle": "2021-03-18T08:36:40.199839Z",
     "shell.execute_reply": "2021-03-18T08:36:40.200557Z"
    },
    "papermill": {
     "duration": 0.049822,
     "end_time": "2021-03-18T08:36:40.200807",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.150985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\" and dataset == \"CIFAR100\": v_type = \"topgd\"\n",
    "    \n",
    "names = [\"teacher\",\"distillation\",\"extract-label\",\"extract-logit\",\"zero-shot\",\"fine-tune\",\"pre-act-18\",\"independent\"]\n",
    "trains = {}\n",
    "tests = {}\n",
    "for name in names:\n",
    "    trains[name] = (torch.load(f\"{root}/model_{name}_normalized/train_{v_type}_vulnerability.pt\"))\n",
    "    tests[name] = (torch.load(f\"{root}/model_{name}_normalized/test_{v_type}_vulnerability.pt\"))\n",
    "mean_cifar = trains[\"teacher\"].mean(dim = (0,1))\n",
    "std_cifar = trains[\"teacher\"].std(dim = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "russian-peoples",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.279496Z",
     "iopub.status.busy": "2021-03-18T08:36:40.279061Z",
     "iopub.status.idle": "2021-03-18T08:36:40.305573Z",
     "shell.execute_reply": "2021-03-18T08:36:40.304539Z"
    },
    "papermill": {
     "duration": 0.067116,
     "end_time": "2021-03-18T08:36:40.305785",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.238669",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\":\n",
    "    for name in names:\n",
    "        trains[name] = trains[name].sort(dim = 1)[0]\n",
    "        tests[name] = tests[name].sort(dim = 1)[0]\n",
    "        \n",
    "for name in names:\n",
    "    trains[name] = (trains[name]- mean_cifar)/std_cifar\n",
    "    tests[name] = (tests[name]- mean_cifar)/std_cifar\n",
    "    \n",
    "f_num = 30\n",
    "a_num=30\n",
    "\n",
    "trains_n = {}\n",
    "tests_n = {}\n",
    "for name in names:\n",
    "    trains_n[name] = trains[name].T.reshape(1000,f_num)[:,:a_num]\n",
    "    tests_n[name] = tests[name].T.reshape(1000,f_num)[:,:a_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "separate-technique",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.381487Z",
     "iopub.status.busy": "2021-03-18T08:36:40.380578Z",
     "iopub.status.idle": "2021-03-18T08:36:40.384513Z",
     "shell.execute_reply": "2021-03-18T08:36:40.383838Z"
    },
    "papermill": {
     "duration": 0.042024,
     "end_time": "2021-03-18T08:36:40.384673",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.342649",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_ex = split_index\n",
    "train = torch.cat((trains_n[\"teacher\"][:n_ex], tests_n[\"teacher\"][:n_ex]), dim = 0)\n",
    "y = torch.cat((torch.zeros(n_ex), torch.ones(n_ex)), dim = 0)\n",
    "\n",
    "rand=torch.randperm(y.shape[0])\n",
    "train = train[rand]\n",
    "y = y[rand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "european-elements",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.455433Z",
     "iopub.status.busy": "2021-03-18T08:36:40.454816Z",
     "iopub.status.idle": "2021-03-18T08:36:40.458272Z",
     "shell.execute_reply": "2021-03-18T08:36:40.457895Z"
    },
    "papermill": {
     "duration": 0.037627,
     "end_time": "2021-03-18T08:36:40.458372",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.420745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = nn.Sequential(nn.Linear(a_num,100),nn.ReLU(),nn.Linear(100,1),nn.Tanh())\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer =torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "funded-wilson",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.534200Z",
     "iopub.status.busy": "2021-03-18T08:36:40.533415Z",
     "iopub.status.idle": "2021-03-18T08:36:43.640142Z",
     "shell.execute_reply": "2021-03-18T08:36:43.636470Z"
    },
    "papermill": {
     "duration": 3.146152,
     "end_time": "2021-03-18T08:36:43.640315",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.494163",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef61deacc4674e679ffa2fec80ac208e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tqdm(range(1000)) as pbar:\n",
    "    for epoch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        inputs = train\n",
    "        outputs = model(inputs)\n",
    "        loss = -1 * ((2*y-1)*(outputs.squeeze(-1))).mean()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        pbar.set_description('loss {}'.format(loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-hindu",
   "metadata": {
    "papermill": {
     "duration": 0.115679,
     "end_time": "2021-03-18T08:36:43.835024",
     "exception": false,
     "start_time": "2021-03-18T08:36:43.719345",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "framed-invitation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:43.966367Z",
     "iopub.status.busy": "2021-03-18T08:36:43.962412Z",
     "iopub.status.idle": "2021-03-18T08:36:43.968411Z",
     "shell.execute_reply": "2021-03-18T08:36:43.968911Z"
    },
    "papermill": {
     "duration": 0.078266,
     "end_time": "2021-03-18T08:36:43.969084",
     "exception": false,
     "start_time": "2021-03-18T08:36:43.890818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_p(outputs_train, outputs_test):\n",
    "    pred_test = outputs_test[:,0].detach().cpu().numpy()\n",
    "    pred_train = outputs_train[:,0].detach().cpu().numpy()\n",
    "    tval, pval = ttest_ind(pred_test, pred_train, alternative=\"greater\", equal_var=False)\n",
    "    if pval < 0:\n",
    "        raise Exception(f\"p-value={pval}\")\n",
    "    return pval\n",
    "\n",
    "def get_p_values(num_ex, train, test, k):\n",
    "    total = train.shape[0]\n",
    "    sum_p = 0\n",
    "    p_values = []\n",
    "    positions_list = []\n",
    "    for i in range(k):\n",
    "        positions = torch.randperm(total)[:num_ex]\n",
    "        p_val = get_p(train[positions], test[positions])\n",
    "        positions_list.append(positions)\n",
    "        p_values.append(p_val)\n",
    "    return p_values\n",
    "\n",
    "def get_fischer(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return combine_pvalues(p_values, method=\"mudholkar_george\")[1]\n",
    "\n",
    "def get_max_p_value(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return max(p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "twenty-stations",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.071630Z",
     "iopub.status.busy": "2021-03-18T08:36:44.071054Z",
     "iopub.status.idle": "2021-03-18T08:36:44.087754Z",
     "shell.execute_reply": "2021-03-18T08:36:44.088124Z"
    },
    "papermill": {
     "duration": 0.069179,
     "end_time": "2021-03-18T08:36:44.088271",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.019092",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "outputs_tr = {}\n",
    "outputs_te = {}\n",
    "for name in names:\n",
    "    outputs_tr[name] = model(trains_n[name])\n",
    "    outputs_te[name] = model(tests_n[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "pretty-occurrence",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.177103Z",
     "iopub.status.busy": "2021-03-18T08:36:44.176359Z",
     "iopub.status.idle": "2021-03-18T08:36:44.177982Z",
     "shell.execute_reply": "2021-03-18T08:36:44.178599Z"
    },
    "papermill": {
     "duration": 0.05679,
     "end_time": "2021-03-18T08:36:44.178790",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.122000",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "def print_inference(outputs_train, outputs_test):\n",
    "    m1, m2 = outputs_test[:,0].mean(), outputs_train[:,0].mean()\n",
    "    pval = get_p(outputs_train, outputs_test)\n",
    "    print(f\"p-value = {pval} \\t| Mean difference = {m1-m2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "australian-manitoba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.280190Z",
     "iopub.status.busy": "2021-03-18T08:36:44.279465Z",
     "iopub.status.idle": "2021-03-18T08:36:44.283610Z",
     "shell.execute_reply": "2021-03-18T08:36:44.282607Z"
    },
    "papermill": {
     "duration": 0.053084,
     "end_time": "2021-03-18T08:36:44.283818",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.230734",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for name in names:\n",
    "    outputs_tr[name], outputs_te[name] = outputs_tr[name][split_index:], outputs_te[name][split_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "failing-helena",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.369339Z",
     "iopub.status.busy": "2021-03-18T08:36:44.368721Z",
     "iopub.status.idle": "2021-03-18T08:36:44.380259Z",
     "shell.execute_reply": "2021-03-18T08:36:44.380768Z"
    },
    "papermill": {
     "duration": 0.05746,
     "end_time": "2021-03-18T08:36:44.380931",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.323471",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teacher\n",
      "p-value = 9.471827244290652e-67 \t| Mean difference = 0.8518033027648926\n",
      "distillation\n",
      "p-value = 7.00831746709577e-35 \t| Mean difference = 0.5558339357376099\n",
      "extract-label\n",
      "p-value = 1.1670189413330969e-30 \t| Mean difference = 0.469926118850708\n",
      "extract-logit\n",
      "p-value = 1.3932107627373622e-43 \t| Mean difference = 0.5647839307785034\n",
      "zero-shot\n",
      "p-value = 9.505112444868077e-22 \t| Mean difference = 0.42093271017074585\n",
      "fine-tune\n",
      "p-value = 1.9153199821774074e-65 \t| Mean difference = 0.8286807537078857\n",
      "pre-act-18\n",
      "p-value = 1.5391024251650987e-42 \t| Mean difference = 0.6405919790267944\n",
      "independent\n",
      "p-value = 0.14952548002422886 \t| Mean difference = 0.061785608530044556\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print(f\"{name}\")\n",
    "    print_inference(outputs_tr[name], outputs_te[name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blond-payment",
   "metadata": {
    "papermill": {
     "duration": 0.042437,
     "end_time": "2021-03-18T08:36:44.474227",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.431790",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Main Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "silver-yahoo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.555383Z",
     "iopub.status.busy": "2021-03-18T08:36:44.554944Z",
     "iopub.status.idle": "2021-03-18T08:36:44.710987Z",
     "shell.execute_reply": "2021-03-18T08:36:44.710210Z"
    },
    "papermill": {
     "duration": 0.204537,
     "end_time": "2021-03-18T08:36:44.711151",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.506614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(f\"{dataset}_{v_type}.h5\"):\n",
    "    total_reps = 40\n",
    "    max_m = 45\n",
    "    total_inner_rep = 100\n",
    "\n",
    "    m_list = [x for x in range(2, max_m, 1)]\n",
    "    p_values_all_threat_models_dict = {}\n",
    "\n",
    "    n_pbar = tqdm(names, leave=False)\n",
    "    for name in n_pbar:\n",
    "        p_vals_per_rep_no = {}\n",
    "        r_pbar = tqdm(range(total_reps), leave=False)\n",
    "        for rep_no in r_pbar:\n",
    "            p_values_list = []\n",
    "            for m in m_list:\n",
    "                p_list = get_p_values(m, outputs_tr[name], outputs_te[name], total_inner_rep)\n",
    "                try:\n",
    "                    hm = hmean(p_list)\n",
    "                except:\n",
    "                    hm = 1.0\n",
    "                p_values_list.append(hm)\n",
    "                pbar.set_description(f\"{rep_no: 2d} {name} m={m: 3d}: f{hm: 0.5f}\")\n",
    "            r_pbar.set_description(f\"rep_no: {rep_no+1}/{total_reps}\")\n",
    "            p_vals_per_rep_no[rep_no] = p_values_list\n",
    "        p_values_all_threat_models_dict[name] = p_vals_per_rep_no\n",
    "        \n",
    "    df_list = []\n",
    "    for name, rep_dict in p_values_all_threat_models_dict.items():\n",
    "        df = pd.DataFrame(rep_dict).reset_index().assign(m=lambda r: r.index+2).drop([\"index\"], axis=1)\n",
    "        df_list.append(pd.melt(df,id_vars=[\"m\"], var_name=\"rep_no\", value_name=\"p_value\").assign(threat_model=name))\n",
    "    results_df = pd.concat(df_list)\n",
    "    \n",
    "    results_df.to_hdf(f\"{dataset}_{v_type}.h5\", v_type)\n",
    "    results_df\n",
    "else:\n",
    "    results_df = pd.read_hdf(f\"{dataset}_{v_type}.h5\", v_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-hamburg",
   "metadata": {
    "papermill": {
     "duration": 0.0324,
     "end_time": "2021-03-18T08:36:44.803461",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.771061",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "previous-clearing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.871529Z",
     "iopub.status.busy": "2021-03-18T08:36:44.870941Z",
     "iopub.status.idle": "2021-03-18T08:36:44.873682Z",
     "shell.execute_reply": "2021-03-18T08:36:44.873114Z"
    },
    "papermill": {
     "duration": 0.039042,
     "end_time": "2021-03-18T08:36:44.873811",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.834769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['text.latex.preamble'] = r'\\usepackage{times}'\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-attitude",
   "metadata": {
    "papermill": {
     "duration": 0.031304,
     "end_time": "2021-03-18T08:36:44.942376",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.911072",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "engaging-theory",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.014811Z",
     "iopub.status.busy": "2021-03-18T08:36:45.014170Z",
     "iopub.status.idle": "2021-03-18T08:36:45.016072Z",
     "shell.execute_reply": "2021-03-18T08:36:45.016542Z"
    },
    "papermill": {
     "duration": 0.039937,
     "end_time": "2021-03-18T08:36:45.016721",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.976784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rename_dict = {\n",
    "    \"threat_model\": \"Threat Model\", \n",
    "    \"teacher\": \"Source\", \n",
    "    \"distillation\": \"Distillation\", \n",
    "    \"extract-label\": \"Label-Query\", \n",
    "    \"extract-logit\": \"Logit-Query\", \n",
    "    \"zero-shot\": \"Zero-Shot Learning\",\n",
    "    \"fine-tune\": \"Fine-Tuning\",\n",
    "    \"pre-act-18\": \"Diff. Architecture\",\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "junior-being",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.090855Z",
     "iopub.status.busy": "2021-03-18T08:36:45.090398Z",
     "iopub.status.idle": "2021-03-18T08:36:45.124173Z",
     "shell.execute_reply": "2021-03-18T08:36:45.123142Z"
    },
    "papermill": {
     "duration": 0.070648,
     "end_time": "2021-03-18T08:36:45.124420",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.053772",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_df = (results_df.rename(columns=rename_dict).replace(rename_dict)\n",
    "           .query('(m >= 5)')\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "hourly-cloud",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.206227Z",
     "iopub.status.busy": "2021-03-18T08:36:45.205282Z",
     "iopub.status.idle": "2021-03-18T08:36:45.209502Z",
     "shell.execute_reply": "2021-03-18T08:36:45.208668Z"
    },
    "papermill": {
     "duration": 0.045149,
     "end_time": "2021-03-18T08:36:45.209684",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.164535",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sizes\n",
    "SMALL_SIZE = 14\n",
    "MEDIUM_SIZE = SMALL_SIZE + 1\n",
    "BIGGER_SIZE = MEDIUM_SIZE + 1\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsi ze of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dirty-vulnerability",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.301990Z",
     "iopub.status.busy": "2021-03-18T08:36:45.299608Z",
     "iopub.status.idle": "2021-03-18T08:36:51.608738Z",
     "shell.execute_reply": "2021-03-18T08:36:51.609469Z"
    },
    "papermill": {
     "duration": 6.360963,
     "end_time": "2021-03-18T08:36:51.609710",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.248747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'p-value')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n",
    "g = sns.lineplot(x=\"m\", y=\"p_value\", hue=\"Threat Model\", data=plot_df, ax=ax, ci=99)\n",
    "g.axhline(0.05, ls='--', color=\"black\", alpha=0.4)\n",
    "g.axhline(0.01, ls='--', color=\"black\", alpha=0.4)\n",
    "g.set_xlabel(\"Number of Sample Revealed\")\n",
    "g.set_ylabel(\"p-value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-microphone",
   "metadata": {
    "papermill": {
     "duration": 0.046349,
     "end_time": "2021-03-18T08:36:51.704749",
     "exception": false,
     "start_time": "2021-03-18T08:36:51.658400",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "opposed-inquiry",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:51.802221Z",
     "iopub.status.busy": "2021-03-18T08:36:51.801677Z",
     "iopub.status.idle": "2021-03-18T08:36:52.262742Z",
     "shell.execute_reply": "2021-03-18T08:36:52.262178Z"
    },
    "papermill": {
     "duration": 0.510979,
     "end_time": "2021-03-18T08:36:52.262887",
     "exception": false,
     "start_time": "2021-03-18T08:36:51.751908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46dc6138f12745829812aea599f65d30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import generate_table\n",
    "tab = generate_table(selected_m=10, outputs_te=outputs_te, outputs_tr=outputs_tr, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dedicated-proceeding",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:52.362924Z",
     "iopub.status.busy": "2021-03-18T08:36:52.362378Z",
     "iopub.status.idle": "2021-03-18T08:36:52.365594Z",
     "shell.execute_reply": "2021-03-18T08:36:52.365068Z"
    },
    "papermill": {
     "duration": 0.054521,
     "end_time": "2021-03-18T08:36:52.365712",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.311191",
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      "               mean_diff  p_value\n",
      "teacher            0.838    0.000\n",
      "distillation       0.586    0.000\n",
      "pre-act-18         0.645    0.000\n",
      "zero-shot          0.371    0.024\n",
      "fine-tune          0.832    0.000\n",
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      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "{} & mean_diff &    p_value \\\\\n",
      "\\midrule\n",
      "teacher       &     0.838 & $10^{-04}$ \\\\\n",
      "distillation  &     0.586 & $10^{-04}$ \\\\\n",
      "pre-act-18    &     0.645 & $10^{-04}$ \\\\\n",
      "zero-shot     &     0.371 & $10^{-02}$ \\\\\n",
      "fine-tune     &     0.832 & $10^{-05}$ \\\\\n",
      "extract-label &     0.475 & $10^{-03}$ \\\\\n",
      "extract-logit &     0.563 & $10^{-03}$ \\\\\n",
      "independent   &     0.103 & $10^{+00}$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
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